Top Challenges GBS Organizations Face in Scaling and Implementing Generative AI Solutions | Market Insights™
November 21, 2024
GBS Organizations
GBS Organizations
This move comes amid speculation that OpenAI may be considering an initial public offering and as its recently appointed CFO has been actively touting the company’s strategy.
OpenAI’s fresh infusion of capital can help the company to accelerate research in cutting-edge AI technologies like GPT-4 and to acquire or build larger and more powerful computing infrastructure, among other areas, according to Nitish Mittal, Partner in the technology practice of Everest Group, a research firm.
“AI models require immense computational resources to train and operate,” he said in an emailed statement.
Read more at: CFO Dive
A global professional services and solutions firm delivering outcomes that shape the future, hosted its AI Day today, bringing together numerous clients and partners in New York City to learn about the latest advances in generative AI, machine learning, data, and analytics.
“Most companies are just talking about technology tools and experimentation – Genpact has put words into action and taken this a step further today with its showcase of real-world case studies of AI in use with clients on stage,” said Manu Aggarwal, Partner, Everest Group. “It was eye-opening to gain insights into the company’s practitioner point of view, solutions for AI adoption at scale, and its focus on domain expertise combined with technology to scale AI.”
Read more at: Financial Times
Two big shifts are under way in the world of software development. Since the launch of ChatGPT in 2022, bosses have been falling over themselves to try to find ways to use generative AI (gen AI). Most efforts have yielded little, but one exception is programming. Surveys suggest that developers around the world find generative AI so useful that already about two-fifths of them use it.
Read more about what Everest Group Vice President Alisha Mittal had to say on the matter at: The Economist
Enterprises are all in on AI. They want their models to run in production environments smoothly and with as high performance as possible to obtain a high return on investment. However, even with all the advanced models available in the market, teams continue to struggle with deployment issues.
Last year, Peter Bendor-Samuel, the CEO of Everest Group, estimated that 90% of the gen AI pilots started will not make it to production.
Read more at: VentureBeat
In today’s digital-first world, customer expectations have evolved rapidly…
Modern customers now expect fast, accurate, and personalized interactions from the brands they engage with. Furthermore, meeting these heightened expectations has become a challenge for businesses, driving the adoption of advanced technologies to enhance customer engagement.
At the forefront of these technologies is Conversational AI (CAI), an increasingly transformative solution reshaping how companies interact with their customers.
In this blog, we will explore how CAI technology is revolutionizing engagement across the entire customer journey, and how businesses should integrate CAI into their tech stack for providing an efficient, scalable, and personalized engagement to the modern customer.
CAI has been one of the biggest beneficiaries of the AI revolution over the past decade. Early solutions were rule-based, functioning on pre-programmed scripts that limited their ability to adapt to diverse inquiries or provide truly personalized service.
Today’s AI-powered bots can use sophisticated Machine Learning (ML) algorithms to understand context, intent, and sentiment, enabling more natural and engaging interactions across the plethora of channels that exist i.e. voice, chat, email, and social media.
Now with the addition of generative AI (gen AI) and the ability to effectively leverage customer data, CAI bots have grown more adept at handling complex queries, offering dynamic and customized responses, often with limited human intervention.
One of the most impactful aspects of CAI is in its true versatility i.e. its ability to assist customers at every stage of their journey, from initial engagement through to post-purchase support. From the moment potential customers discover a brand, CAI bots can engage with them in real time 24/7, as explained below.
Generating high-quality leads is one of the most crucial tasks for sales and marketing teams. CAI can enhance lead generation efforts by engaging potential customers on websites or social media channels in real time. Through outbound campaigns, they can gather essential data and seamlessly hand off qualified leads to sales teams
Instead of browsing through static menus or endless product categories, users can rely on conversational search to find what they’re looking for faster. CAI systems, especially when integrated with enterprise applications like customer relationship management (CRMs) and customer data platforms (CDPs), can analyze user preferences, behavior, and past interactions across various channels
CAI can provide insights on bundle deals, warranty options, and related products, helping customers make informed purchase decisions. If a customer hesitates at checkout, the chatbot can step in with timely offers or discounts to encourage completion of the purchase. Furthermore, these chatbots seamlessly integrate with payment gateways like PayPal and Apple Pay, allowing secure transactions directly within the chat interface, adhering to industry-standard security protocols
CAI can conveniently help customers with order confirmation, receipt generation, and next steps such as shipping details. It enables brands to check in with customers, asking about their experience and offering tips for maximizing product use. The chatbot can also assist customers with returns, refunds, and exchanges making the process hassle-free
CAI can schedule follow-up interactions with customers after they’ve left, sending personalized emails or messages highlighting new features, improvements, or exclusive return offers. Automating win-back efforts ensures the brand maintains a connection and demonstrates a commitment to addressing any previous issues.
To illustrate the comprehensive support CAI provides, the following exhibit showcases how a potential customer navigates a fictional e-commerce website, TechTrends, that has embraced CAI across the customer journey.
While CAI presents significant opportunities for businesses, successful implementation requires thoughtful planning and execution. The following best practices are recommended to successfully implement and harness the capabilities of CAI.
CAI’s capabilities can transform what was once a series of disjointed transactions into a fluid, intuitive, and highly personalized customer journey.
This streamlined approach saves time for the customer, increases conversion rates for the business, and ultimately creates a more satisfying and efficient experience.
Looking ahead, the future of CAI is poised for remarkable advancements. CAI bots will evolve into agentic systems, becoming autonomous digital colleagues, capable of higher-order planning and independent decision-making.
Through the combination of deep learning and reinforcement learning, these systems will be able to process large amounts of data, recognize complex patterns, and learn from their actions and experiences in real-time environments.
The bottom line for enterprise leaders remains the same, conversational AI’s real impact is not just in introducing it in a siloed fashion, but embedding it deeply across the customer journey, into the core of business processes, where it can be of deliverable measurable value.
If you have any questions, would like to delve deeper into the Experience, Sustainability & Trust market, or would like to reach out to discuss these topics in more depth, please contact Simran Agrawal ([email protected]) and Anubhav Das ([email protected])
After experiencing significant growth post-pandemic, the customer experience management (CXM) market hit turbulence in 2024. Enterprises are now more cautious about their spending, pushing service providers to do more with less. At the same time, generative AI (gen AI)-led use cases are moving into production, which is revolutionizing how contact center leaders are thinking about their future operating models.
Watch this webinar to hear our CXM experts examine how the CXM market has evolved throughout 2024, and share what can be expected for 2025.
What questions did the webinar answer?
Who should attend?
Remember when we were all buzzing about the metaverse like it was going to redefine reality? Yeah, that was just two years ago!
Fast forward to last year, and suddenly generative AI (gen AI) has become the rockstar, spinning up content faster than we can say “machine learning.”
Now, as if we have blinked and missed a beat, we’re already asking, “what’s next?” – Enter Agentic AI, poised to not just assist, but act autonomously…
Could this be the game-changer for Life Sciences? Our expert analysts have found out just what this means for the sector going into 2025 and beyond!
Reach out to discuss this topic in depth.
Agentic AI is an evolved form of AI that creates autonomous agents possessing autonomy, decision-making, and adaptability. The agents can execute tasks in their entirety through natural language-based inputs. They can also set goals independently, plan accordingly, and act to accomplish the targets.
Key characteristics of agentic AI include:
The winning formula for agentic AI is training the models on diverse datasets with clear and concise instructions.
Life sciences has always been a curious case for any emerging and next-generation technology – as it has always presented a unique challenge when it comes to adopting emerging technologies, whether it was Robotic Process Automation (RPA) a decade ago, cloud computing five years ago, or gen AI more recently, enterprises often display initial enthusiasm, diving into exploratory use cases and early proof of concepts (POCs).
However, as inherent challenges such as regulatory concerns, data privacy, and integration complexities emerge, majority enterprises take a step back and adopt a more cautious approach. This cycle reflects the industry’s general mindset—embracing innovation with enthusiasm, but always tempered by a significant degree of caution
Similarly, the industry is gradually transitioning from a cautious to a more pragmatic approach when it comes to adopting gen AI across various areas.
As enterprises continue to advance in this journey, Agentic AI can act as a powerful catalyst—particularly in targeted areas/segments—by driving efficiencies and accelerating time to return on investment (ROI). By automating decision-making and improving engagement processes, Agentic AI can help organizations realize the full potential of AI adoption faster and with greater impact.
While everyone was buzzing about “top use cases” in 2023, 2024 is all about getting strategic with scaled tech (hello, Gen AI!). Furthermore, just like its predecessor, Agentic AI is set to follow a similar trajectory—but expect this journey to be much faster.
In fact, there are a handful of areas where we predict Agentic AI will make the biggest splash in record time. So, without further ado, here are the top areas where Agentic AI will hit the ground running and deliver results in the short to medium term.
A key challenge with Agentic AI is understanding how it differs from existing conversational tools, such as chatbots and conversational assistants, which are steadily maturing in their capabilities.
This distinction is not just theoretical but critical, as each technology serves vastly different purposes. The complexity lies in unraveling these differences in both functionality and impact.
To simplify, the table below outlines the fundamental contrasts between chatbots, conversational assistants, and AI agents, with a focus on their technological architecture, autonomy, and practical use in life sciences. By illustrating these nuances, we can appreciate how AI agents go beyond basic interaction to deliver intelligent, autonomous decision-making in dynamic, real-world environments.
Agentic AI, however, shifts away from this hybrid model by significantly reducing or eliminating human involvement, relying instead on autonomous multi-agent interactions to manage decisions and workflows. For life sciences organizations, this presents a challenge: the need to develop a greater risk appetite and embrace potentially human-less frameworks. Successfully adopting Agentic AI will require not only trust in the technology, but also a shift in mindset, as companies learn to cede control to AI systems capable of operating independently.
In conclusion, Agentic AI presents a transformative potential for the life sciences industry, pushing the boundaries beyond traditional chatbots and conversational assistants.
However, its adoption will require overcoming industry-specific challenges such as trust, strategic implementation, and the availability of domain-specific training data. As life sciences enterprises gradually embrace this technology, Agentic AI could revolutionize engagement, decision-making, and operational efficiency, but only if organizations are ready to adapt to its autonomous, human-less frameworks.
If you found this blog interesting, check out our blog focusing on The Healthcare Professional (HCP) Engagement Blueprint: Winning Strategies For Building Lasting Connections | Blog – Everest Group , which delves deeper into another topic worked on by our HSL service line.
If you have any questions, would like to gain expertise in Agentic AI and artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Rohit K, Durga Ambati, and Chunky Satija.
©2024 Everest Global, Inc. Privacy Notice Terms of Use Do Not Sell My Information Research Participation Terms
"*" indicates required fields
